Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Education
Reference69 articles.
1. Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)
2. Allison, L., Dix, T.I.: A bit-string longest-common-subsequence algorithm. Inf. Process. Lett. 23(5), 305–310 (1986)
3. Aucouturier, J.J., Pachet, F., et al.: Music similarity measures: what’s the use? In: Proceedings of ISMIR ’02 (2002)
4. Beel, J., Langer, S.: A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems. In: Proceedings of TPDL ’15 (2015)
5. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cited by
26 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A personalized ranking method based on inverse reinforcement learning in search engines;Engineering Applications of Artificial Intelligence;2024-10
2. Examining the merits of feature-specific similarity functions in the news domain using human judgments;User Modeling and User-Adapted Interaction;2024-08-07
3. Incorporating Editorial Feedback in the Evaluation of News Recommender Systems;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27
4. Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics;Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-22
5. User Perceptions of Diversity in Recommender Systems;Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-22